Deepfake Detection Analysis
', unsafe_allow_html=True) st.markdown('Upload original and modified images to analyze potential deepfakes
', unsafe_allow_html=True) # Create columns for file uploaders col1, col2 = st.columns(2) with col1: st.markdown('Original Image
', unsafe_allow_html=True) original_file = st.file_uploader("Upload the original image", type=["jpg", "jpeg", "png"]) with col2: st.markdown('Modified Image
', unsafe_allow_html=True) modified_file = st.file_uploader("Upload the potentially modified image", type=["jpg", "jpeg", "png"]) # Parameters for analysis st.markdown('Analysis Parameters
', unsafe_allow_html=True) col1, col2 = st.columns(2) with col1: threshold = st.slider("Difference Threshold", min_value=10, max_value=100, value=30, help="Higher values detect only significant differences") with col2: min_area = st.slider("Minimum Detection Area", min_value=50, max_value=500, value=100, help="Minimum area size to consider as a modified region") # Process images when both are uploaded if original_file and modified_file: # Create temporary directory for processing with tempfile.TemporaryDirectory() as temp_dir: # Save uploaded files to temporary directory original_path = os.path.join(temp_dir, "original.jpg") modified_path = os.path.join(temp_dir, "modified.jpg") with open(original_path, "wb") as f: f.write(original_file.getbuffer()) with open(modified_path, "wb") as f: f.write(modified_file.getbuffer()) # Create output directory output_dir = os.path.join(temp_dir, "output") os.makedirs(output_dir, exist_ok=True) # Process images and generate visualization with st.spinner("Processing images and generating analysis..."): # Initialize components detector = DeepfakeDetector() labeler = ThreatLabeler() heatmap_gen = HeatmapGenerator() # Step 1: Verification Module - Process the image pair detection_results = detector.process_image_pair(original_path, modified_path, threshold, min_area) # Step 2: Labeling System - Label detected regions by threat level original_image = img_processor.load_image(original_path) modified_image = img_processor.load_image(modified_path) labeled_image, labeled_regions = labeler.label_regions( original_image, detection_results['difference_image'], detection_results['bounding_boxes']) # Get threat summary threat_summary = labeler.get_threat_summary(labeled_regions) # Step 3: Heatmap Visualization - Generate heatmaps for threat visualization heatmap_overlay = heatmap_gen.generate_threat_heatmap(original_image, labeled_regions) multi_heatmaps = heatmap_gen.generate_multi_level_heatmap(original_image, labeled_regions) # Combine all results all_results = { 'original_image': original_image, 'modified_image': modified_image, 'difference_image': detection_results['difference_image'], 'threshold_image': detection_results['threshold_image'], 'annotated_image': detection_results['annotated_image'], 'labeled_image': labeled_image, 'heatmap_overlay': heatmap_overlay, 'multi_heatmaps': multi_heatmaps, 'threat_summary': threat_summary, 'smi_score': detection_results['smi_score'], 'bounding_boxes': detection_results['bounding_boxes'] } # Create output directory in a permanent location output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "comparison_output") os.makedirs(output_dir, exist_ok=True) # Generate unique filename based on original image base_name = os.path.splitext(original_file.name)[0] output_path = os.path.join(output_dir, f"{base_name}_combined_overlay.png") # Create and save combined visualization with heatmaps from combined_visualization import CombinedVisualizer visualizer = CombinedVisualizer() combined_results = { 'original_image': original_image, 'difference_image': detection_results['difference_image'], 'bounding_boxes': detection_results['bounding_boxes'], 'multi_heatmaps': multi_heatmaps, 'labeled_regions': labeled_regions } combined_path = visualizer.create_combined_visualization( combined_results, output_path, alpha_diff=0.4, alpha_low=0.3, alpha_medium=0.4 ) # Create and save comparison visualization grid_output_path = os.path.join(output_dir, f"{base_name}_comparison.png") comparison.create_comparison_grid(all_results, grid_output_path) # Display the comprehensive visualization st.markdown('Comprehensive Analysis
', unsafe_allow_html=True) st.image(grid_output_path, use_container_width=True) # Display threat summary st.markdown('Threat Summary
', unsafe_allow_html=True) st.markdown(f"**SMI Score:** {detection_results['smi_score']:.4f} (1.0 = identical, 0.0 = completely different)") st.markdown(f"**Total regions detected:** {threat_summary['total_regions']}") # Create columns for threat counts col1, col2, col3 = st.columns(3) with col1: st.markdown(f"**Low threats:** {threat_summary['threat_counts']['low']}") with col2: st.markdown(f"**Medium threats:** {threat_summary['threat_counts']['medium']}") with col3: st.markdown(f"**High threats:** {threat_summary['threat_counts']['high']}") if threat_summary['max_threat']: st.markdown(f"**Maximum threat:** {threat_summary['max_threat']['level'].upper()} ({threat_summary['max_threat']['percentage']:.1f}%)") st.markdown(f"**Average difference:** {threat_summary['average_difference']:.1f}%") else: # Display instructions when images are not yet uploaded st.info("Please upload both original and modified images to begin analysis.") # Display sample image st.markdown('Sample Analysis Output
', unsafe_allow_html=True) sample_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "comparison_output", "deepfake1_comparison.png") if os.path.exists(sample_path): st.image(sample_path, use_container_width=True) st.caption("Sample analysis showing all detection stages in a single comprehensive view") else: st.write("Sample image not available. Please upload images to see the analysis.")